Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea.
ImmunoBiome Inc., Pohang, 37666, Korea.
Institute of Convergence Science, Yonsei University, Seoul, 03722, Korea.
在过去的几年中，免疫检查点抑制剂 (ICI) 大大提高了癌症患者的生存率。然而，只有少数患者对 ICI 治疗有反应（实体瘤中约为 30%），而当前 ICI 反应相关的生物标志物通常无法预测 ICI 治疗反应。在这里，我们提出了一个机器学习 (ML) 框架，该框架利用基于网络的分析来识别可以做出稳健预测的 ICI 治疗生物标志物 (NetBio)。我们整理了 700 多个 ICI 治疗的患者样本以及临床结果和转录组数据，并观察到基于 NetBio 的预测准确地预测了三种不同癌症类型（黑色素瘤、胃癌和膀胱癌）的 ICI 治疗反应。此外，基于 NetBio 的预测优于基于其他传统 ICI 治疗生物标志物的预测，例如 ICI 靶点或肿瘤微环境相关标志物。这项工作提出了一种基于网络的方法，可以有效地选择免疫治疗反应相关的生物标志物，可以为精准肿瘤学做出基于机器学习的稳健预测。
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types—melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.
